code: def run_eval(args):
      logging.set_verbosity(logging.WARNING)
  
      args = utils.dict_to_namedtuple(args)
  
      config = hparams_config.get_efficientdet_config(args.model_name)
      config.override(args.hparams, allow_new_keys=True)
      config.image_size = utils.parse_image_size(config.image_size)
  
      params = dict(config.as_dict(), seed=None)
  
      logging.info(params)
  
      utils.setup_gpus()
  
      dataset = utils.get_dataset(args, 1, False, params, None)
  
      model = efficientdet_net.EfficientDetNet(params=params)
      model.compile()
  
      if args.weights:
          image_size = params["image_size"]
          model.predict(np.zeros((1, image_size[0], image_size[1], 3)))
          model.load_weights(args.weights)
  
      model.evaluate(dataset, steps=args.eval_steps)
  
file_path: DALI/docs/examples/use_cases/tensorflow/efficientdet/eval.py
imports: import multiprocessing
  from absl import logging
  import numpy
  import tensorflow
  import re
  import os
  import hparams_config
  import utils
  from model import efficientdet_net
label_desc: Use image node_id 1 for calling a global function with example usage: run_eval(args) without return types